Daniel Zaldivar1, Yvette Bohraus2, Nikos Logothetis2,3,4, and Jozien Goense5,6,7
1LN, National Institute of Mental Health, Bethesda, MD, United States, 2Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany, 3University of Manchester, Manchester, United Kingdom, 4International Center for Primate Brain Research, Shanghai, China, 5Department of Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States, 6Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 7Beckman Institute for Advanced Science and Technology, Urbana, IL, United States
Synopsis
Keywords: Brain Connectivity, fMRI, Laminar fMRI and neurophysiology
How
accurately fMRI reflects the underlying laminar differences in neural
processing? In the current study we investigated the relationship between neural activity and fMRI signals across different cortical layers. We found layer and frequency dependent differences in neural activity during the presentation of visual stimulus that elicits positive and negative BOLD response.
Laminar fMRI aims to study
how information is processed between and within different cortical layers and regions.
However, questions remain how accurately fMRI reflects the underlying laminar
differences in neural processing. We compared laminar fMRI and
electrophysiological responses to rotating checkerboard visual stimuli that
elicit PBR and NBR. We found that during PBR the overall correspondence between
the fMRI signals and local field potentials (LFP) was mostly confined within
the gamma frequency range with no particular dependence on one cortical layer
or fMRI modality. In contrast, for the NBR stimulus, the CBV showed good
correspondence with the LFP as both LFP-power and CBV increased in middle
layers while both signals decreased in superficial and deep layers.Introduction
Functional MRI at the resolution of cortical layers (laminar fMRI) offers
a novel window into activation patterns across the cortical sheet and allows us
to ask fundamental questions about functional architecture, neurovascular coupling,
and neuronal circuit dynamics. However, there are important outstanding
questions about the correspondence between the fMRI signal and underlying
neural activity at the laminar level. Here we investigated how changes in the
laminar hemodynamic responses are linked to the underlying neurophysiological
responses by comparing the depth dependent time courses of different fMRI- and
neurophysiological signals.Methods
fMRI data were acquired at 4.7T (Bruker vertical scanner) and separate
laminar electrophysiological experiments (probe of 16 linearly arranged
contacts spaced 150 µm, NeuroNexus, Figure 1B) were performed on a total of
twelve anesthetized monkeys. We presented visual stimuli in a block-design
paradigm that elicits positive- (PBR) and negative BOLD responses (NBR; Figure
1A), and acquired BOLD, CBV, CBF and electrophysiological responses. We
computed the relative power of the local field potentials (LFP; 0.5-150 Hz)
across the different cortical layers (Figure 1B). The precise location of the
laminar probe was determined based on current-source-density (CSD) analysis which
is defined as the second spatial derivative of LFP signals (Figure 1B, left
panel). The input layers can be identified as the location on the earliest
current sink (white arrow). Boundaries between middle and deep layers were
determined by computing the intercompartmental coherence at the gamma LFP range
(right panel). Deep layers lack coherence with superficial and middle layers
providing an additional marker for depthResults
Figure 1D shows the time courses from each fMRI signal across cortical
depth in response to PBR and NBR stimuli. The time courses for the BOLD, CBF
and CBV responses showed laminar differences, with earliest onset of CBF and
CBV responses in granular layers. The neurophysiological response to the PBR indicates
an increase in LFP power in superficial and middle layers across all frequency
bands although the LFP power increase was found to be the largest at the gamma
range. During the NBR stimuli the CBV response increased in middle layers which
corresponded to an increase in the gamma power in the same cortical layer. This
neurophysiological characteristic was also accompanied by a strong decrease in gamma
power in superficial layers. A similar spatial distribution of LFP power across
layers in response to the NBR stimulus was observed in other frequency bands. That
is, a power increase in both low and mid frequency ranges was observed to be
confined to middle layers while superficial layers strongly decreased. A post
stimulus undershoot/overshoot was not much evident in our neurophysiology results,
which leads us to suggest a hemodynamic origin of the different laminar fMRI post
stimulus responses.Discussion
We show frequency and layer dependent differences in neural activity
during the PBR and NBR conditions. During the PBR there was no obvious
(one-to-one) relationship between the power of LFP bands and the amplitude of
the fMRI signals across layers, although all layers showed signal increases
(both fMRI and neurophysiology). Such a relationship was more evident during
the NBR where we found the profile of neural activity to correlate with the CBV
profile. This suggests that despite visual information reaching the main
thalamic recipient layer (middle layer) during NBR, this does not elicit an
increased response in the other layers, although the changes in neural activity
do trigger an increase in CBV. This may be due, partly, to the strong
inhibitory control elicited by feedback inputs targeting superficial and deep
layers. The decrease in LFP power seen in deep and superficial layers during
the NBR resembles that seen under top-down modulation of V1 activity and this
may represent feedback processing3. This is supported by anatomical
studies showing feedback projections preferentially target superficial and deep
layers, while avoiding middle layers4,5. Acknowledgements
No acknowledgement found.References
1. Goense et al., Neuron
76:629-639 (2012); 2. Shmuel et al.,
Nat. Neurosci 9:569-577 (2006); 3. Van Kerkoerle et al.,
Nat Commun 8, 13804 (2017); 4. Markov et al., J Comp Neurol 522, 225-259
(2014); 5. Henry, et al., Eur J Neurosci 3, 186-200 (1991).